Automatic identification of flowcell irregularities

Abstract

Methods can be used to analyze a biological sample flow. These methods may comprise flowing a biological sample through a flowcell, and capturing a plurality of images of the biological sample. The method may also comprise identifying a position in the flowcell of each of a plurality of particles from the biological sample, and applying a map function to each of the plurality of particles. This may result in a plurality of map functions, and those map functions may then be used for identifying irregular flow areas in the flowcell.

Description

AUTOMATIC IDENTIFICATION OF FLOWCELLIRREGULARITIESCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This claims the benefit of, and is an international application of provisional patent application 63 / 713,021, titled “Automatic Identification of Flowcell Irregularities” and filed in the U.S. patent and trademark office on October 28, 2024, the disclosure of which is hereby incorporated by reference in its entirety.BACKGROUND

[0002] Blood cell analysis is one of the most commonly performed medical tests for providing an overview of a patient's health status. A blood sample can be drawn from a patient's body and stored in a test tube containing an anticoagulant to prevent clotting. A whole blood sample normally comprises three major classes of blood cells including red blood cells (erythrocytes), white blood cells (leukocytes) and platelets (thrombocytes). Each class can be further divided into subclasses of members. For example, five major types or subclasses of white blood cells (WBCs) have different shapes and functions. White blood cells may include neutrophils, lymphocytes, monocytes, eosinophils, and basophils. There are also subclasses of the red blood cell types. The appearances of particles in a sample may differ according to pathological conditions, cell maturity and other causes. Red blood cell subclasses may include reticulocytes and nucleated red blood cells.

[0003] In a flowcell based blood cell analysis system, preferably, particles will be uniformly distributed in the sample stream as it flows through the flowcell, for example, because this can be useful in determining the concentrations of various types of particles in a patient sample.- 1 -01337880810418 4896-4881-5720vlHowever, various issues, such as manufacturing defects or damage to a flowcell, can interfere with this type of uniform distribution. Accordingly, there is a need for technology which can automatically identify irregularities in a flowcell which can interfere with the uniform distribution of particles in patient samples.SUMMARY

[0004] Aspects of the present disclosure may be used in determining the presence of irregularities in a flowcell based on cell imaging system. In some aspects, the disclosed technology may be used to implement a method of analyzing a biological sample flow. Such a method may comprise flowing a biological sample through a flowcell, and capturing a plurality of images of the biological sample. The method may also comprise identifying a position in the flowcell of each of a plurality of particles from the biological sample, and applying a map function to each of the plurality of particles. This may result in a plurality of map functions, and those map functions may then be used for identifying irregular flow areas in the flowcell. Other implementations, such as corresponding systems and analyzers may also be implemented based on this disclosure.

[0005] It should be noted that any of the various features of the aspects disclosed herein can be included or combined in each of those aspects. Other aspects and implementations are described herein. Accordingly, the exemplary aspects described in this summary should be understood as being illustrative only and should not be treated as limiting.BRIEF DESCRIPTION OF THE DRAWINGS

[0006] While the specification concludes with claims which particularly point out and distinctly claim the invention, it is believed the present invention will be better understood from the following description of certain examples taken in conjunction with the accompanying drawings, in which like reference numerals identify the same elements and in which:- 2 -01337880810418 4896-4881-5720vl

[0007] FIG. 1 is a schematic illustration, partly in section and not to scale, showing operational aspects of an exemplary flow cell which may be used in some implementations.

[0008] FIG. 2 illustrates a method which may be used to identify irregularities.

[0009] FIG. 3 illustrates how positions of particles in a flowcell may be identified.

[0010] FIG. 4 illustrates a mass distribution.

[0011] FIG. 5 illustrates a preparatory step of combining map functions into a map.

[0012] FIG. 6 illustrates a mass map.

[0013] FIG. 7 depicts steps that may be performed in an implementation which dynamically determines a threshold.

[0014] FIG. 8 depicts how particles representations can be combined into a single image.

[0015] FIG. 9 depicts a process which may be used for identifying gravity values.

[0016] FIG. 10 depicts approaches which may be used in determining distances for identifying a gravity value.

[0017] FIG. 11 illustrates a gravity distribution.

[0018] FIG. 12 illustrate a gravity map.

[0019] FIG. 13 illustrates steps which may be performed to identify irregular flow areas.

[0020] FIG. 14 illustrates an approach to determining if there is an irregularity of the flow of particles through a flowcell.

[0021] The drawings are not intended to be limiting in any way, and it is contemplated that various embodiments of the invention may be carried out in a variety of other ways, including those- 3 -01337880810418 4896-4881-5720vlnot necessarily depicted in the drawings. The accompanying drawings incorporated in and forming a part of the specification illustrate several aspects of the present invention, and together with the description serve to explain the principles of the invention; it being understood, however, that this invention is not limited to the precise arrangements shown.DETAILED DESCRIPTION

[0022] The present disclosure relates to articles, systems, and methods for analyzing the flow of a biological sample in a biological analyzer. In some aspects, the analyzer may be a visual analyzer comprising one or more processors to facilitate automated conversion and / or analysis of images. Such analyzers may be useful, for example, in characterizing particles in biological fluids, such as detecting and quantifying erythrocytes, reticulocytes, nucleated red blood cells, platelets, and white blood cells, including white blood cell differential counting, categorization and subcategorization and analysis. Other similar uses such as characterizing blood cells from other fluids (serum, bone marrow, lavage fluid, effusions, exudates, cerebrospinal fluid, pleural fluid, peritoneal fluid, and amniotic fluid) are also contemplated.

[0023] Turning now to the drawings, FIG. 1 schematically shows an exemplary flow cell 22 which may be used in an analyzer for conveying a sample fluid through a viewing zone 23 of a high optical resolution imaging device 24 (e.g., a camera) in a configuration for imaging microscopic particles in a sample flow stream 32 using digital image processing. Flow cell 22 is coupled to a source 25 of sample fluid which may have been subjected to processing, such as contact with a particle contrast agent composition and heating. Flow cell 22 is also coupled to one or more sources 27 of a particle and / or intracellular organelle alignment liquid (PIO AL) / sheath fluid, such as a clear glycerol solution having a viscosity that is greater than the viscosity of the sample fluid, an example of which is disclosed in U.S. Pat. Nos. 9,316,635 and 10,451,612, the disclosures of which are hereby incorporated by reference in their entirety.

[0024] The sample fluid is injected through a flattened opening at a distal end 28 of a sample feed tube 29, and into the interior of the flow cell 22 at a point where the PIO AL flow has been- 4 -01337880810418 4896-4881-5720vlsubstantially established resulting in a stable and symmetric laminar flow of the PIOAL above and below (or on opposing sides of) the ribbon-shaped sample stream. The sample and PIO AL streams may be supplied by precision metering pumps that move the PIO AL with the injected sample fluid along a flow path that narrows substantially. The PIOAL envelopes and compresses the sample fluid in the zone 21 where the flow path narrows. Hence, the decrease in flow path thickness at zone 21 can contribute to a geometric focusing of the sample flow stream 32. The sample flow stream 32 is enveloped and carried along with the PIOAL downstream of the narrowing zone 21, passing in front of, or otherwise through the viewing zone 23 of, the high optical resolution imaging device 24 where images are collected, for example, using a Charge-Coupled Device (CCD) 48 observing the sample stream as illuminated using an illumination source 42 through a viewing port 57. Processor 18 can receive, as input, pixel data from CCD 48. The sample fluid ribbon flows together with the PIOAL to a discharge 33.

[0025] As shown here, the narrowing zone 21 can have a proximal flow path portion 21a having a proximal thickness PT and a distal flow path portion 21b having a distal thickness DT, such that distal thickness DT is less than proximal thickness PT. The sample fluid can therefore be injected through the distal end 28 of sample tube 29 at a location that is distal to the proximal portion 21a and proximal to the distal portion 21b. Hence, the sample fluid can enter the PIOAL envelope as the PIOAL stream is compressed by the zone 21, wherein the sample fluid injection tube has a distal exit port through which sample fluid is injected into flowing sheath fluid, the distal exit port bounded by the decrease in flow path size of the flow cell.

[0026] The digital high optical resolution imaging device 24 with objective lens 46 is directed along an optical axis that intersects the ribbon-shaped sample flow stream 32. The relative distance between the objective 46 and the flow cell 33 is variable by operation of a motor drive 54, for resolving and collecting a focused digitized image on a photosensor array. Additional information regarding the construction and operation of an exemplary flow cell such as shown in FIG. 1 is provided in U.S. Patent 9,322,752, entitled “Flow cell Systems and Methods for- 5 -01337880810418 4896-4881-5720vlParticle Analysis in Blood Samples,” filed on March 17, 2014, the disclosure of which is hereby incorporated by reference in its entirety.

[0027] In operation, the disclosed technology may be used to identify irregularities in a flowcell such as shown in FIG. 1 using a method such as illustrated in FIG. 2. Initially, in the method of FIG. 2, a biological sample comprising a plurality of particles may be flowed 201 through a flowcell, such as in the form of a sample stream as described above in the context of FIG. 1. As the particles are being flowed 201 through the flowcell, a plurality of images of those particles may be captured 202. These images may be captured in the form of “frames” - or nonoverlapping images of the sample stream. Each frame may depict zero, one, or more of the particles, though preferably the patient sample will be appropriately diluted and flowed through the flowcell such that each frame will depict a single particle. Once the images of the particles have been captured 202, the positions of the particles in the flowcell may be identified 203. As shown in FIG. 3, this may be done by identifying 301 predefined flowcell coordinates for each of the plurality of images. That is, in some implementations of the disclosed technology, a flowcell may have a coordinate system which would be the same for each of the images captured while a sample stream is flowing through the flowcell. The locations of each of the particles may then be identified 301 in the flowcell’s coordinate system, thereby providing a universal reference frame, which could be used for purposes such as combining various images and / or associating map functions with regions of a flowcell.

[0028] Continuing with the method of FIG. 2, in that method, after the positions of the particles had been identified 203, a map function could be applied 204 to each of the particles, thereby resulting in a plurality of map functions for the biological sample in which the particles had been included. To illustrate how this may be done, consider a case where the map function is a bell-shaped mass function. In such a case, the particles may be considered as a set {Po, Pi, . . . Pk} where k is the total number of imaged particles, and each particle Pi has coordinates (e.g., in a predefined flowcell coordinate system such as described in the context of FIG. 3) of (xi, yi). In this case, the map function may be any suitable bell-shaped function centered on the- 6 -01337880810418 4896-4881-5720vlparticles’ coordinates. For example, in some cases a map function such as shown in equation 1 (below) may be used.Equation 1, in which M(x, y) is a mass map function, (x, y) is a location in the flowcell, and w is a value (e.g., 10) which controls the spread of the function M(x, y).With this type of function, applying 204 the function to a particular particle could be done by substituting that particle’s coordinates for Xi and yi in the above equation. This would provide a mass distribution for that particle such as the mass distribution of FIG. 4, which illustrates the result of applying the map function of equation 1 to a single particle with coordinates (100, 200).

[0029] In the method of FIG. 2, once the map function had been applied 204 to the plurality of particles, the resulting plurality of map functions (e.g., mass distributions such as shown in FIG. 4) could be used to identify 205 irregular flow areas in the flowcell. To continue the above example, in a case where applying 204 the map function was done by applying 204 the bellshaped mass function of equation 1 to each of the captured particles, utilizing the plurality of map functions to identify 205 irregular flow areas may have a preparatory step such as shown in FIG. 5 of combining 401 the plurality of map functions into a map. This may be done using the particles’ coordinates, for example, by superimposing the map functions of each of the particles onto a single image (e.g., adding the values of each particle’s map function at each location in the image) such as the mass map shown in FIG. 6. The map combining the map functions could then be used to identify 205 areas of flow irregularity in the flow cell. For instance, in some cases, the value of each point in the map could be compared to a threshold (e.g., a predefined threshold determined in advance based on the expected concentrations of particles in biological samples) and any points above the threshold could be treated as indicating a flow irregularity at the corresponding location in the flowcell.- 7 -01337880810418 4896-4881-5720vl

[0030] Variations are also possible in how the plurality of maps may be used to identify irregular flow areas in a flowcell. To illustrate, consider FIG. 7, which depicts steps that may be performed in an implementation which dynamically determines a threshold for identifying flow irregularities, rather than relying on a predetermined threshold such as described previously. In an implementation following FIG. 7, the coordinates of the imaged particles would be mapped 501 into a combined image. This could be done by identifying 301 coordinates for each of the imaged particles in a predefined flowcell coordinate frame as described above in the context of FIG. 3, and then combining those particles into a single image in that reference frame, such as the image shown in FIG. 8, in which each particle is mapped onto a single opaque dot with a size determined based on the expected concentration of particles in a sample. Aggregations of particles around a flow area could then be identified 502, such as using blob detection or other methods of analyzing the coordinates and distribution of the particles in the combined image. This aggregation information could then be used to determine 503 a threshold for identifying the irregular flow areas in the flowcell. For example, the different sizes of the identified aggregations (e.g., one particle, two particles, etc.) could be plotted on a histogram, with the peak of that histogram and the values in the neighborhood of the peak being treated as representing regular flow, and the first local maximum to the right of the neighborhood of the peak being treated as a threshold for identifying an irregularity. This threshold could then be used for identifying 205 irregular flow areas in the flowcell in a manner similar to that described above for the mapping function and a predefined threshold.

[0031] Another example of a potential variation in how a plurality of maps may be used to identify irregular flow areas is to use a gravity map function rather than a mass map function or a dot map function such as described above. In an implementation which uses a gravity map function, each particle may have a gravity value identified for it using a process such as shown in FIG. 9. Specifically, identifying a particle’s gravity value may include calculating 601 the distances (e.g., Euclidian distances, Manhattan distances, Minkowski distances, or some other type of distance metric) between that particular and each other particle in the plurality of particles, then determining 602 a set of those distances which would be used for identifying- 8 -01337880810418 4896-4881-5720vlthat particle’s gravity value. As shown in FIG. 10, this may be done in a variety of manners. For example, in some cases, determining 602 the distances may be done by identifying 701 the distances less than a parameter (e.g., 5, or some other predefined value which could be determined based on expected concentration) as being the distances that should be included in the set of distances. However, in other cases, the determination 602 may comprise identifying 702 the n smallest distances (where n is some integer value that may be determined based on a number of particles which are expected to be in proximity to each other, such as 10) as being the distances that should be included in the set of distances.

[0032] Continuing with the discussion of FIG. 9, once the set of distances from a particle to other particles from the plurality of particles had been determined, a gravity value could be calculated 603 for each of those distances. This may be done using a calculation such as equation 2:Equation 2, in which g(dk) is the gravity value for distance dk, dk is the kthdistance in the set of distances, and s is a factor which defines how quickly the gravity value decreases with distance (e.g., 2).Then, after gravity values had been calculated 603 for each of the distances to the particles which were closest to the particle for which the process of FIG. 9 was being performed, a mean of those gravity values could be identified 604. For example, if the set of distances determined 602 for a particular particle included j distances, then the gravity value identified for that particular particle could be the mean of the various gravity values as defined by equation 3 :- 9 -01337880810418 4896-4881-5720vlEquation 3This could be represented for each particle as a gravity distribution, an example of which is provided in FIG. 11. The gravity distributions for each of the particles (each of which gravity distributions may be considered a map function corresponding to the associated particle) may then, as illustrated in FIG. 13, be associated 801 with regions in the flowcell, such as by combining them into a gravity map, such as the gravity map illustrated in FIG. 12, in the reference frame of the flowcell. The values in the gravity map could then have a threshold applied 802 (e.g., a dynamically determined threshold, or a predetermined threshold, as described previously in the context of FIGS. 6 and 7) to identify irregular flow areas in the flowcell.

[0033] It is also possible that the disclosed technology may be implemented in a manner which does not rely on map functions and position determinations such as those described above. As an illustration of this, consider FIG. 14, which illustrates an alternative approach to determining if there is an irregularity of the flow of particles through a flowcell. Initially, a method implemented using the approach of FIG. 14 would be similar to methods described above, with a sample being flowed 201 through a flowcell and images of the sample being captured 202. However, rather than identifying 203 positions of particles in those images, an implementation following FIG. 14 could combine 901 frames depicting the sample’s particles, thereby resulting in a large rectangular image having dimensions of the width of a frame and the product of the height of a frame and the number of frames captured, such as shown in FIG. 15. A plurality of segmentations for that large rectangle could then be determined 902. For instance, using the exemplary combined image rectangle of FIG. 15, to detect irregular flow in the Y direction, the rectangle could be split into a first set of M segments and a second set of P segments. Each of the segments in each of those sets may or may not overlap with one or more other segments in its set, could have the same width as the frames used for imaging the particles (i.e., frame_width, using the labels from FIG. 15), and may also have the same height- 10 -01337880810418 4896-4881-5720vlas all of the other segments, which can be denoted Ysegment (which may be the same as, greater than, or less than, the height of the frames used for imaging the particles).

[0034] In the process of FIG. 14, once a plurality of segmentations had been determined 902 for the combined image rectangle, the number of particles in each of the segments can be counted 903, and the counts for at least one of the sets of segments can be used to calculate statistics regarding the concentration of particles in the combined image rectangle when considered at the scale of individual segments. For example, in some cases, where the sets of segments included a first set of M segments, the average number of particles in each of the M segments and the standard deviation of the numbers of particles in the M segments can be calculated to provide statistics representing how many particles can be expected to be seen in a segment of size frame width x Ysegment and how much deviation can be seen in the number of particles between segments. These statistics can then be compared 905 with the particle counts from another set to determine if there is a flow irregularity in the flowcell. For example, if a combined image rectangle was split into a first set of M segments and a second set of P segments, then statistics for the first set could be compared to particle counts for segments in the second set using an expression such asMaverage—P * Mstd P1' Maverage + P * MstdEquation 4, in which Maverage is the average number of particles in a segment from the first set of segments, Mstd is the standard deviation of number of particles per segment in the first set of segments, i is an integer value which ranges from 0 to P, pi is the ithsegment in the second set of segments, and P is a constant (e.g., 3) which determines how sensitive the comparison is to irregularities.Using this type of comparison, if the actual particle counts had excessive divergence from the expected particle counts (e.g., if the expression set forth above as equation 4 was false) it could be treated as indicating an irregularity, and a notification could be provided of that irregularity- 11 -0133788 0810418 4896-4881-5720vlso that appropriate action could be taken (e.g., maintenance could be initiated to remove the irregularity from the flowcell).

[0035] Other variations and potential applications are also possible and will be immediately apparent to one of skill in the art based on this disclosure. Accordingly, the examples set forth herein should be understood as illustrative only, and should not be treated as implying limitations on the scope of protection provided by this document or any related document. Additionally, to further illustrate potential variations, the following examples are provided for how the disclosed technology may potentially be implemented in practice.

[0036] Example 1

[0037] A method for analyzing a biological sample flow, comprising: providing a flowcell configured to receive a biological sample that is flowed through the flowcell, the biological sample comprising a plurality of particles; capturing a plurality of images of the biological sample; identifying a position of each of the plurality of particles in the flowcell; applying a map function to each of the plurality of particles, thereby resulting in a plurality of map functions; and utilizing the plurality of map functions for identifying irregular flow areas in the flowcell.

[0038] Example 2

[0039] The method of example 1, wherein identifying the position comprises a step of identifying predefined flowcell coordinates of each of the plurality of particles.

[0040] Example 3

[0041] The method of example 2, comprising: mapping the coordinates of each of the plurality of particles into a combined image; identifying an aggregation of the particles around a flow area by analyzing the coordinates and distribution of the particles in the combined image; and determining a threshold for identifying the irregular flow areas by using the identified aggregation information.- 12 -01337880810418 4896-4881-5720vl

[0042] Example 4

[0043] The method of any of examples 2-3, wherein the method comprises combining the plurality of map functions into a map using the coordinates of each of the plurality of particles and wherein the map includes a gravity map.

[0044] Example 5

[0045] The method of example 4, comprising, for each of one or more particles from the plurality of particles: for each of a plurality of other particles, calculating a distance between that particle and that other particle in the gravity map; determining a set of the calculated distances based on magnitudes of the calculated distances; calculating gravity values of each distance from the determined set of calculated distances; and identifying a mean of the calculated gravity values.

[0046] Example 6

[0047] The method of example 5, wherein, for each of the one or more particles from the plurality of particles, determining the set of calculated distances based on magnitudes of the calculated distances comprises: identifying calculated distance less than a predetermined parameter as being the set of calculated distances; or identifying a predefined number of smallest calculated distances as being the set of calculated distances.

[0048] Example 7

[0049] The method of any of examples 1-6, wherein the map function includes a gravity map function.

[0050] Example 8

[0051] The method of any of examples 2-3, wherein the method comprises combining the plurality of map functions into a map using the coordinates of each of the plurality of particles and wherein the map includes a mass map.- 13 -01337880810418 4896-4881-5720vl

[0052] Example 9

[0053] The method of any of examples 1-3 or 8, wherein the map function includes a mass map function.

[0054] Example 10

[0055] The method of any of examples 1-9, wherein each of the plurality of images of the biological sample includes one particle.

[0056] Example 11

[0057] The method of any of examples 1-10, wherein utilizing the plurality of map functions to identify irregular flow areas in the flowcell comprises: associating the plurality of map functions with regions of the flowcell; and applying a threshold for identifying the irregular flow areas.

[0058] Example 12

[0059] The method of example 11, wherein the threshold is a pre-defined threshold.

[0060] Example 13

[0061] A system for analyzing a biological sample flow, comprising: at least one processor; and a non-transitory computer readable medium having stored thereon instruction which, when executed by the at least one processor, cause the system to perform the method of any of examples 1-12.

[0062] Example 14

[0063] An analyzer comprising: at least one processor; and a non-transitory computer readable medium stored thereon instruction which, when executed by the at least one processor, cause the analyzer to perform the method of any of examples 1-12.- 14 -01337880810418 4896-4881-5720vl

[0064] Example 15

[0065] A system for analyzing a biological sample flow, comprising: a flowcell configured to receive a biological sample; an image capture device configured to capture a plurality of images of the biological sample; at least one processor; and a computer-readable medium storing instructions that are configured to, when executed by the at least one processor: receive the a plurality of images of the biological sample; identify a position of each of the plurality of particles in the flowcell; apply a map function to each of the plurality of particles, thereby resulting in a plurality of map functions; and utilize the plurality of map functions for identifying irregular flow areas in the flowcell.

[0066] Example 16

[0067] The system of example 15, wherein identifying the position comprises a step of identifying predefined flowcell coordinates of each of the plurality of particles.

[0068] Example 17

[0069] The system of any of examples 15-16, wherein the instructions are configured to, when executed by the at least one processor: map the coordinates of each of the plurality of particles into a combined image; identify an aggregation of the particles around a flow area by analyzing the coordinates and distribution of the particles in the combined image; and determine a threshold for identifying the irregular flow areas by using the identified aggregation information.

[0070] Example 18

[0071] The system of any of examples 15-17, wherein the instructions are configured to, when executed by the at least one processor, combine the plurality of map functions into a map using the coordinates of each of the plurality of particles and wherein the map includes a gravity map.- 15 -01337880810418 4896-4881-5720vl

[0072] Example 19

[0073] The system of example 18, wherein the instructions are configured to, when executed by the at least one processor, for each of one or more particles from the plurality of particles: for each of a plurality of other particles, calculate a distance between that particle and that other particle in the gravity map; determine a set of the calculated distances based on magnitudes of the calculated distances; calculate gravity values of each distance from the determined set of calculated distances; and identify a mean of the calculated gravity values.

[0074] Example 20

[0075] The system of example 19, wherein, for each of the one or more particles from the plurality of particles, determining the set of calculated distances based on magnitudes of the calculated distances comprises: identifying calculated distance less than a predetermined parameter as being the set of calculated distances; or identifying a predefined number of smallest calculated distances as being the set of calculated distances.

[0076] Example 21

[0077] The system of any of examples 15-20 wherein the map function includes a gravity map function.

[0078] Example 22

[0079] The system of any of examples 15-17, wherein the instructions are configured to, when executed by the at least one processor: combine the plurality of map functions into a map using the coordinates of each of the plurality of particles and wherein the map includes a mass map.

[0080] Example 23- 16 -01337880810418 4896-4881-5720vl

[0081] The system of any of examples 15-17 or 22, wherein the map function includes a mass map function.

[0082] Example 24

[0083] The system of any of examples 15-23, wherein each of the plurality of images of the biological sample includes one particle.

[0084] Example 25

[0085] The system of any of examples 15-24, wherein utilizing the plurality of map functions to identify irregular flow areas in the flowcell comprises: associating the plurality of map functions with regions of the flowcell; and applying a threshold for identifying the irregular flow areas.

[0086] Example 26

[0087] The system of any of examples 15-25, wherein the threshold is a pre-defined threshold.

[0088] Example 27

[0089] A method comprising performing the set of acts the instructions stored on the one or more non- transitory computer readable mediums of examples 15-26 are to perform when executed.

[0090] Example 28

[0091] An analyzer, comprising: at least one processor; and a non-transitory computer readable medium stored thereon instruction which, when executed by the at least one processor, cause the biological analyzer to perform the set of acts the instructions stored on the one or more non-transitory computer readable mediums of the system of any of examples 15-26 are to perform when executed.

[0092] Example 29- 17 -01337880810418 4896-4881-5720vl

[0093] An analyzer comprising: a flowcell configured to receive a biological sample; and at least one processor configured to: capture a plurality of images of the biological sample; identify a position of each of the plurality of particles in the flowcell; apply a map function to each of the plurality of particles, thereby resulting in a plurality of map functions; and utilize the plurality of map functions for identifying irregular flow areas in the flowcell.

[0094] Example 30

[0095] The analyzer of example 29, wherein identifying the position comprises a step of identifying predefined flowcell coordinates of each of the plurality of particles.

[0096] Example 31

[0097] The analyzer of any of examples 29-30, wherein the at least one processor is configured to: map the coordinates of each of the plurality of particles into a combined image; identify an aggregation of the particles around a flow area by analyzing the coordinates and distribution of the particles in the combined image; and determine a threshold for identifying the irregular flow areas by using the identified aggregation information.

[0098] Example 32

[0099] The analyzer of any of examples 29-31, wherein the at least one processor is configured to combine the plurality of map functions into a map using the coordinates of each of the plurality of particles and wherein the map includes a gravity map.

[0100] Example 33

[0101] The analyzer of any of examples 29-32, wherein the at least one processor is configured to, for each of one or more particles from the plurality of particles: for each of a plurality of other particles, calculate a distance between that particle and that other particle in the gravity map; determine a set of the calculated distances based on magnitudes of the calculated distances;- 18 -01337880810418 4896-4881-5720vlcalculate gravity values of each distance from the determined set of calculated distances; and identify a mean of the calculated gravity values.

[0102] Example 34

[0103] The analyzer of any of examples 29-33, wherein, for each of the one or more particles from the plurality of particles, determining the set of calculated distances based on magnitudes of the calculated distances comprises: identifying calculated distance less than a predetermined parameter as being the set of calculated distances; or identifying a predefined number of smallest calculated distances as being the set of calculated distances.

[0104] Example 35

[0105] The analyzer of any of examples 29-34, wherein the map function includes a gravity map function.

[0106] Example 36

[0107] The system of any of examples 29-31, wherein the at least one processor is configured to combine the plurality of map functions into a map using the coordinates of each of the plurality of particles and wherein the map includes a mass map.

[0108] Example 37

[0109] The system of any of examples 29-31 or 36, wherein the map function includes a mass map function.

[0110] Example 38

[0111] The system of any of examples 29-37, wherein each of the plurality of images of the biological sample includes one particle.

[0112] Example 39- 19 -01337880810418 4896-4881-5720vl

[0113] The system of any of examples 29-38, wherein utilizing the plurality of map functions to identify irregular flow areas in the flowcell comprises: associating the plurality of map functions with regions of the flowcell; and applying a threshold for identifying the irregular flow areas.

[0114] Example 40

[0115] The system of example 39, wherein the threshold is a pre-defined threshold.

[0116] Example 41

[0117] A system for analyzing a biological sample flow, comprising: at least one processor; and a non-transitory computer readable medium having stored thereon instruction which, when executed by the at least one processor, cause the system to perform any of examples 29-40.

[0118] Example 42

[0119] A method comprising performing the set of acts the instructions stored on the one or more non- transitory computer readable mediums of claims 29-40 are to perform when executed.

[0120] Each of the calculations or operations described herein may be performed using a computer or other processor having hardware, software, and / or firmware. The various method steps may be performed by modules, and the modules may comprise any of a wide variety of digital and / or analog data processing hardware and / or software arranged to perform the method steps described herein. The modules optionally comprising data processing hardware adapted to perform one or more of these steps by having appropriate machine programming code associated therewith, the modules for two or more steps (or portions of two or more steps) being integrated into a single processor board or separated into different processor boards in any of a wide variety of integrated and / or distributed processing architectures. These methods and systems will often employ a tangible media embodying machine-readable code with instructions for performing the method steps described above. Suitable tangible media may- 20 -01337880810418 4896-4881-5720vlcomprise a memory (including a volatile memory and / or a non-volatile memory), a storage media (such as a magnetic recording on a floppy disk, a hard disk, a tape, or the like; on an optical memory such as a CD, a CD-R / W, a CD-ROM, a DVD, or the like; or any other digital or analog storage media), or the like.

[0121] All patents, patent publications, patent applications, journal articles, books, technical references, and the like discussed in the instant disclosure are incorporated herein by reference in their entirety for all purposes.

[0122] Different arrangements of the components depicted in the drawings or described above, as well as components and steps not shown or described are possible. For example, in some cases, aspects of processing described herein (e.g., determination of a morphology score, use of such a score to evaluate a patient’s health, use of such a score to assess cell alignment in a flowcell) may be performed in various configurations - for instance, using a processor which is comprised by (or local to) an analyzer, a parallel-processing arrangement, or processing being performed remotely from the analyzer which captures the processed images (such as using a cloud based platform, or using a remotely linked computer or system to process the analyzer results). Similarly, some features and sub-combinations are useful and may be employed without reference to other features and sub-combinations. Embodiments of the invention have been described for illustrative and not restrictive purposes, and alternative embodiments will become apparent to readers of this patent. In certain cases, method steps or operations may be performed or executed in differing order, or operations may be added, deleted or modified. It can be appreciated that, in certain aspects of the invention, a single component may be replaced by multiple components, and multiple components may be replaced by a single component, to provide an element or structure or to perform a given function or functions. Except where such substitution would not be operative to practice certain embodiments of the invention, such substitution is considered within the scope of the invention. Accordingly, the claims should not be treated as limited to the examples, drawings, embodiments and illustrations provided above, but instead should be understood as having the scope provided when their terms are- 21 -01337880810418 4896-4881-5720vlgiven their broadest reasonable interpretation as provided by a general -purpose dictionary, except that when a term or phrase is indicated as having a particular meaning under the heading Explicit Definitions, it should be understood as having that meaning when used in the claims.

[0123] Explicit Definitions

[0124] It should be understood that, in the above examples and the claims, a statement that something is “based on” something else should be understood to mean that it is determined at least in part by the thing that it is indicated as being based on. To indicate that something must be completely determined based on something else, it is described as being “based EXCLUSIVELY on” whatever it must be completely determined by.

[0125] It should be understood that, in the above examples and claims, the term “set” should be understood as one or more things which are grouped together. Similarly, “superset” and “subset” should be each be understood as being synonymous with “set,” with the alternative terms “superset” and “subset” being used only for ease of reading. For the avoidance of doubt, a “superset” of a type of item should not be understood as necessarily referring to all items of that type, unless such is explicitly stated. For example, a reference to capturing a “superset” of cell images should not be understood as requiring that the “superset” of cell images are all of the captured cell images. While the “superset” may be all of the captured cell images, this is not necessary, and should not be treated as being implied by the term “superset.”- 22 -01337880810418 4896-4881-5720vl

Claims

We claim:

1. A method for analyzing a biological sample flow, comprising: providing a flowcell configured to receive a biological sample that is flowed through the flowcell, the biological sample comprising a plurality of particles; capturing a plurality of images of the biological sample; identifying a position of each of the plurality of particles in the flowcell; applying a map function to each of the plurality of particles, thereby resulting in a plurality of map functions; and utilizing the plurality of map functions for identifying irregular flow areas in the flowcell.

2. The method of claim 1, wherein identifying the position comprises a step of identifying predefined flowcell coordinates of each of the plurality of particles.

3. The method of claim 1, wherein each of the plurality of images of the biological sample includes one particle.

4. The method of claim 1, wherein the map function includes a mass map function.

5. The method of claim 2, wherein the method comprises combining the plurality of map functions into a map using the coordinates of each of the plurality of particles and wherein the map includes a mass map.

6. The method of claim 2, comprising: mapping the coordinates of each of the plurality of particles into a combined image; identifying an aggregation of the particles around a flow area by analyzing the coordinates and distribution of the particles in the combined image; and- 23 -01337880810418 4896-4881-5720vldetermining a threshold for identifying the irregular flow areas by using the identified aggregation information.

7. The method of claim 1, wherein the map function includes a gravity map function.

8. The method of claim 2, wherein the method comprises combining the plurality of map functions into a map using the coordinates of each of the plurality of particles and wherein the map includes a gravity map.

9. The method of claim 8, comprising, for each of one or more particles from the plurality of particles: for each of a plurality of other particles, calculating a distance between that particle and that other particle in the gravity map; determining a set of the calculated distances based on magnitudes of the calculated distances; calculating gravity values of each distance from the determined set of calculated distances; and identifying a mean of the calculated gravity values.

10. The method of claim 9, wherein, for each of the one or more particles from the plurality of particles, determining the set of calculated distances based on magnitudes of the calculated distances comprises: identifying calculated distance less than a predetermined parameter as being the set of calculated distances; or identifying a predefined number of smallest calculated distances as being the set of calculated distances.- 24 -01337880810418 4896-4881-5720vl11 . The method of claim 1 , wherein utilizing the plurality of map functions to identify irregular flow areas in the flowcell comprises: associating the plurality of map functions with regions of the flowcell; and applying a threshold for identifying the irregular flow areas.

12. The method of claim 11, wherein the threshold is a pre-defined threshold.

13. A system for analyzing a biological sample flow, comprising: at least one processor; and a non-transitory computer readable medium having stored thereon instruction which, when executed by the at least one processor, cause the system to perform the method of any of claims 1- 12.

14. An analyzer, comprising: at least one processor; and a non-transitory computer readable medium stored thereon instruction which, when executed by the at least one processor, cause the analyzer to perform the method of any of claims 1-12.

15. A system for analyzing a biological sample flow, comprising: a flowcell configured to receive a biological sample; an image capture device configured to capture a plurality of images of the biological sample; at least one processor; and a computer-readable medium storing instructions that are configured to, when executed by the at least one processor: receive the plurality of images of the biological sample; identify a position of each of the plurality of particles in the flowcell;- 25 -01337880810418 4896-4881-5720vlapply a map function to each of the plurality of particles, thereby resulting in a plurality of map functions; and utilize the plurality of map functions for identifying irregular flow areas in the flowcell.

16. The system of claim 15, wherein identifying the position comprises a step of identifying predefined flowcell coordinates of each of the plurality of particles.

17. The system of claim 15, wherein each of the plurality of images of the biological sample includes one particle.

18. The system of claim 15, wherein the map function includes a mass map function.

19. The system of claim 16, wherein the instructions are further configured to, when executed by the at least one processor, combine the plurality of map functions into a map using the coordinates of each of the plurality of particles and wherein the map includes a mass map.

20. The system of claim 16, wherein the instructions are further configured to, when executed by the at least one processor: map the coordinates of each of the plurality of particles into a combined image; identify an aggregation of the particles around a flow area by analyzing the coordinates and distribution of the particles in the combined image; and determine a threshold for identifying the irregular flow areas by using the identified aggregation information.

21. The system of claim 15, wherein the map function includes a gravity map function.- 26 -01337880810418 4896-4881-5720vl22. The system of claim 16, wherein instructions are further configured to, when executed by the at least one processor, combine the plurality of map functions into a map using the coordinates of each of the plurality of particles and wherein the map includes a gravity map.

23. The system of claim 22, wherein the instructions are further configured to, when executed by the at least one processor, for each of one or more particles from the plurality of particles: for each of a plurality of other particles, calculate a distance between that particle and that other particle in the gravity map; determine a set of the calculated distances based on magnitudes of the calculated distances; calculate gravity values of each distance from the determined set of calculated distances; and identify a mean of the calculated gravity values.

24. The system of claim 23, wherein, for each of the one or more particles from the plurality of particles, determining the set of calculated distances based on magnitudes of the calculated distances comprises: identifying calculated distance less than a predetermined parameter as being the set of calculated distances; or identifying a predefined number of smallest calculated distances as being the set of calculated distances.

25. The system of claim 15, wherein utilizing the plurality of map functions to identify irregular flow areas in the flowcell comprises: associating the plurality of map functions with regions of the flowcell; and applying a threshold for identifying the irregular flow areas.

26. The system of claim 15, wherein the threshold is a pre-defined threshold.- 27 -01337880810418 4896-4881-5720vl27. A method comprising performing the set of acts the instructions stored on the one or more non-transitory computer readable mediums of claims 15-26 are to perform when executed.

28. An analyzer, comprising: at least one processor; and a non-transitory computer readable medium stored thereon instruction which, when executed by the at least one processor, cause the biological analyzer to perform the set of acts the instructions stored on the one or more non-transitory computer readable mediums of the system of any of claims 15-26 are to perform when executed.

29. An analyzer, comprising: a flowcell configured to receive a biological sample; and at least one processor configured to perform acts comprising: capture a plurality of images of the biological sample; identify a position of each of the plurality of particles in the flowcell; apply a map function to each of the plurality of particles, thereby resulting in a plurality of map functions; and utilize the plurality of map functions for identifying irregular flow areas in the flowcell.

30. The analyzer of claim 29, wherein identifying the position comprises a step of identifying predefined flowcell coordinates of each of the plurality of particles.

31. The analyzer of claim 29, wherein each of the plurality of images of the biological sample includes one particle.

32. The analyzer of claim 29, wherein the map function includes a mass map function.- 28 -01337880810418 4896-4881-5720vl33. The analyzer of claim 29, wherein the at least one processor is further configured to combine the plurality of map functions into a map using the coordinates of each of the plurality of particles and wherein the map includes a mass map.

34. The analyzer of claim 29, wherein the at least one processor is further configured to: map the coordinates of each of the plurality of particles into a combined image; identify an aggregation of the particles around a flow area by analyzing the coordinates and distribution of the particles in the combined image; and determine a threshold for identifying the irregular flow areas by using the identified aggregation information.

35. The analyzer of claim 29, wherein the map function includes a gravity map function.

36. The analyzer of claim 29, wherein the at least one processor is further configured to combine the plurality of map functions into a map using the coordinates of each of the plurality of particles and wherein the map includes a gravity map.

37. The analyzer of claim 32, wherein the at least one processor is further configured to, for each of one or more particles from the plurality of particles: for each of a plurality of other particles, calculate a distance between that particle and that other particle in the gravity map; determine a set of the calculated distances based on magnitudes of the calculated distances; calculate gravity values of each distance from the determined set of calculated distances; and identify a mean of the calculated gravity values.- 29 -01337880810418 4896-4881-5720vl38. The analyzer of claim 37, wherein, for each of the one or more particles from the plurality of particles, determining the set of calculated distances based on magnitudes of the calculated distances comprises: identifying calculated distance less than a predetermined parameter as being the set of calculated distances; or identifying a predefined number of smallest calculated distances as being the set of calculated distances.

39. The analyzer of claim 29, wherein utilizing the plurality of map functions to identify irregular flow areas in the flowcell comprises: associating the plurality of map functions with regions of the flowcell; and applying a threshold for identifying the irregular flow areas.

40. The analyzer of claim 39, wherein the threshold is a pre-defined threshold.

41. A system for analyzing a biological sample flow, comprising: at least one processor; and a non-transitory computer readable medium having stored thereon instruction which, when executed by the at least one processor, cause the system to perform any the acts which the processor of the analyzer of any of claims 29-40 are configured to perform.

42. A method comprising performing the set of acts the instructions stored on the one or more non-transitory computer readable mediums of claims 29-40 are to perform when executed.- 30 -01337880810418 4896-4881-5720vl

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